Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations294536
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.3 MiB
Average record size in memory375.0 B

Variable types

DateTime1
Text2
Numeric11
Categorical1
Boolean1

Alerts

congestion_level has 11275 (3.8%) zeros Zeros
incident_reports has 25270 (8.6%) zeros Zeros

Reproduction

Analysis started2025-04-03 09:58:03.457755
Analysis finished2025-04-03 09:58:22.889411
Duration19.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

date
Date

Distinct952
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum2022-01-01 00:00:00
Maximum2024-08-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-03T15:28:23.228946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:23.330616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct288
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2025-04-03T15:28:23.663266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length20
Mean length9.6796249
Min length3

Characters and Unicode

Total characters2850998
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGomti Nagar
2nd rowIndira Nagar
3rd rowAlambagh
4th rowRajajipuram
5th rowArjunganj
ValueCountFrequency (%)
nagar 28560
 
6.8%
sector 9520
 
2.3%
area 3808
 
0.9%
colony 3808
 
0.9%
park 3808
 
0.9%
road 3405
 
0.8%
bandra 2856
 
0.7%
varachha 2856
 
0.7%
connaught 2856
 
0.7%
more 2856
 
0.7%
Other values (327) 356016
84.7%
2025-04-03T15:28:24.037657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 562085
19.7%
r 227175
 
8.0%
n 163391
 
5.7%
i 145604
 
5.1%
h 127122
 
4.5%
125813
 
4.4%
e 114552
 
4.0%
o 97665
 
3.4%
d 92699
 
3.3%
l 89488
 
3.1%
Other values (52) 1105404
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2850998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 562085
19.7%
r 227175
 
8.0%
n 163391
 
5.7%
i 145604
 
5.1%
h 127122
 
4.5%
125813
 
4.4%
e 114552
 
4.0%
o 97665
 
3.4%
d 92699
 
3.3%
l 89488
 
3.1%
Other values (52) 1105404
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2850998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 562085
19.7%
r 227175
 
8.0%
n 163391
 
5.7%
i 145604
 
5.1%
h 127122
 
4.5%
125813
 
4.4%
e 114552
 
4.0%
o 97665
 
3.4%
d 92699
 
3.3%
l 89488
 
3.1%
Other values (52) 1105404
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2850998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 562085
19.7%
r 227175
 
8.0%
n 163391
 
5.7%
i 145604
 
5.1%
h 127122
 
4.5%
125813
 
4.4%
e 114552
 
4.0%
o 97665
 
3.4%
d 92699
 
3.3%
l 89488
 
3.1%
Other values (52) 1105404
38.8%
Distinct304
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.9 MiB
2025-04-03T15:28:24.202174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length27
Mean length14.619683
Min length3

Characters and Unicode

Total characters4306023
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShaheed Path
2nd rowFaizabad Road
3rd rowKanpur Road (NH-27)
4th rowHardoi Road
5th rowSultanpur Road
ValueCountFrequency (%)
road 226732
31.5%
marg 16184
 
2.3%
main 11874
 
1.7%
path 10472
 
1.5%
ring 8568
 
1.2%
bypass 8568
 
1.2%
nagar 8568
 
1.2%
high 4760
 
0.7%
new 4760
 
0.7%
highway 4760
 
0.7%
Other values (380) 413691
57.5%
2025-04-03T15:28:24.497699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 746128
17.3%
424401
 
9.9%
o 313130
 
7.3%
d 311600
 
7.2%
R 260052
 
6.0%
r 232773
 
5.4%
i 188734
 
4.4%
n 160323
 
3.7%
h 151415
 
3.5%
e 129567
 
3.0%
Other values (54) 1387900
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4306023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 746128
17.3%
424401
 
9.9%
o 313130
 
7.3%
d 311600
 
7.2%
R 260052
 
6.0%
r 232773
 
5.4%
i 188734
 
4.4%
n 160323
 
3.7%
h 151415
 
3.5%
e 129567
 
3.0%
Other values (54) 1387900
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4306023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 746128
17.3%
424401
 
9.9%
o 313130
 
7.3%
d 311600
 
7.2%
R 260052
 
6.0%
r 232773
 
5.4%
i 188734
 
4.4%
n 160323
 
3.7%
h 151415
 
3.5%
e 129567
 
3.0%
Other values (54) 1387900
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4306023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 746128
17.3%
424401
 
9.9%
o 313130
 
7.3%
d 311600
 
7.2%
R 260052
 
6.0%
r 232773
 
5.4%
i 188734
 
4.4%
n 160323
 
3.7%
h 151415
 
3.5%
e 129567
 
3.0%
Other values (54) 1387900
32.2%

traffic_volume
Real number (ℝ)

Distinct59505
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22583.157
Minimum500
Maximum97282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:24.601706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2071.75
Q18912
median20149
Q331956.25
95-th percentile52281.25
Maximum97282
Range96782
Interquartile range (IQR)23044.25

Descriptive statistics

Standard deviation17149.587
Coefficient of variation (CV)0.75939724
Kurtosis1.3946694
Mean22583.157
Median Absolute Deviation (MAD)11474
Skewness1.1195165
Sum6.6515526 × 109
Variance2.9410832 × 108
MonotonicityNot monotonic
2025-04-03T15:28:24.853706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4124 28
 
< 0.1%
3998 26
 
< 0.1%
2982 25
 
< 0.1%
3735 25
 
< 0.1%
2903 25
 
< 0.1%
3360 25
 
< 0.1%
4906 25
 
< 0.1%
3396 24
 
< 0.1%
4443 24
 
< 0.1%
4795 24
 
< 0.1%
Other values (59495) 294285
99.9%
ValueCountFrequency (%)
500 10
< 0.1%
501 13
< 0.1%
502 9
< 0.1%
503 7
< 0.1%
504 6
< 0.1%
505 9
< 0.1%
506 9
< 0.1%
507 11
< 0.1%
508 5
 
< 0.1%
509 3
 
< 0.1%
ValueCountFrequency (%)
97282 1
< 0.1%
95342 1
< 0.1%
94348 1
< 0.1%
94037 1
< 0.1%
93007 1
< 0.1%
92214 1
< 0.1%
92182 1
< 0.1%
91639 1
< 0.1%
91596 1
< 0.1%
91468 1
< 0.1%

average_speed
Real number (ℝ)

Distinct294172
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.164683
Minimum7.1448462
Maximum89.790843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:24.950604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.1448462
5-th percentile15.060228
Q128.065425
median37.971341
Q348.065283
95-th percentile60.959478
Maximum89.790843
Range82.645996
Interquartile range (IQR)19.999858

Descriptive statistics

Standard deviation13.717873
Coefficient of variation (CV)0.35943894
Kurtosis-0.70076642
Mean38.164683
Median Absolute Deviation (MAD)10.002532
Skewness0.040373684
Sum11240873
Variance188.18005
MonotonicityNot monotonic
2025-04-03T15:28:25.064063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 365
 
0.1%
25.39954748 1
 
< 0.1%
52.13222941 1
 
< 0.1%
50.16891983 1
 
< 0.1%
47.07221981 1
 
< 0.1%
62.39131014 1
 
< 0.1%
59.10989855 1
 
< 0.1%
64.92398498 1
 
< 0.1%
48.09766187 1
 
< 0.1%
51.4767105 1
 
< 0.1%
Other values (294162) 294162
99.9%
ValueCountFrequency (%)
7.144846195 1
< 0.1%
10.00009144 1
< 0.1%
10.00016861 1
< 0.1%
10.00035425 1
< 0.1%
10.00045535 1
< 0.1%
10.00124588 1
< 0.1%
10.00265304 1
< 0.1%
10.00273065 1
< 0.1%
10.00321571 1
< 0.1%
10.00417376 1
< 0.1%
ValueCountFrequency (%)
89.79084251 1
< 0.1%
82.12795345 1
< 0.1%
81.94472677 1
< 0.1%
80.48682398 1
< 0.1%
80.23139882 1
< 0.1%
78.76633909 1
< 0.1%
76.82378504 1
< 0.1%
75.86281133 1
< 0.1%
75.53032677 1
< 0.1%
75.12173833 1
< 0.1%

travel_time_index
Real number (ℝ)

Distinct97310
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5863233
Minimum1.0000385
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:25.157619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000385
5-th percentile1.35
Q11.89
median2.5
Q33.0179388
95-th percentile4
Maximum10
Range8.9999615
Interquartile range (IQR)1.1279388

Descriptive statistics

Standard deviation1.0498182
Coefficient of variation (CV)0.40591142
Kurtosis9.5615748
Mean2.5863233
Median Absolute Deviation (MAD)0.56
Skewness2.3356787
Sum761765.33
Variance1.1021182
MonotonicityNot monotonic
2025-04-03T15:28:25.248717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 5794
 
2.0%
2.27 893
 
0.3%
2.4 886
 
0.3%
2.35 881
 
0.3%
2.23 878
 
0.3%
2.48 874
 
0.3%
2.58 859
 
0.3%
2.42 859
 
0.3%
2.25 858
 
0.3%
2.28 855
 
0.3%
Other values (97300) 280899
95.4%
ValueCountFrequency (%)
1.000038548 1
< 0.1%
1.000489375 1
< 0.1%
1.00090187 1
< 0.1%
1.000948559 1
< 0.1%
1.000986878 1
< 0.1%
1.001029193 1
< 0.1%
1.001208349 1
< 0.1%
1.001591218 1
< 0.1%
1.001674977 1
< 0.1%
1.001749593 1
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9.99 2
 
< 0.1%
9.98 1
 
< 0.1%
9.97 1
 
< 0.1%
9.96 4
< 0.1%
9.95 5
< 0.1%
9.94 4
< 0.1%
9.93 7
< 0.1%
9.92 8
< 0.1%
9.91 5
< 0.1%

congestion_level
Real number (ℝ)

Zeros 

Distinct251987
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.184488
Minimum0
Maximum480.27
Zeros11275
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:25.334789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7789341
Q135.948497
median54.117049
Q374.25
95-th percentile113.5125
Maximum480.27
Range480.27
Interquartile range (IQR)38.301503

Descriptive statistics

Standard deviation40.512428
Coefficient of variation (CV)0.68451091
Kurtosis13.81845
Mean59.184488
Median Absolute Deviation (MAD)19.123699
Skewness2.6484187
Sum17431962
Variance1641.2568
MonotonicityNot monotonic
2025-04-03T15:28:25.436532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11277
 
3.8%
0 11275
 
3.8%
43.5 11
 
< 0.1%
92.98 10
 
< 0.1%
76.71 10
 
< 0.1%
104.93 9
 
< 0.1%
115.49 9
 
< 0.1%
88.41 9
 
< 0.1%
65.84 9
 
< 0.1%
71.18 9
 
< 0.1%
Other values (251977) 271908
92.3%
ValueCountFrequency (%)
0 11275
3.8%
0.002914358794 1
 
< 0.1%
0.003605227847 1
 
< 0.1%
0.005986990683 1
 
< 0.1%
0.006835966621 1
 
< 0.1%
0.01226125943 1
 
< 0.1%
0.01371415267 1
 
< 0.1%
0.01404544642 1
 
< 0.1%
0.01448273965 1
 
< 0.1%
0.01452404526 1
 
< 0.1%
ValueCountFrequency (%)
480.27 1
< 0.1%
479.93 1
< 0.1%
478.21 1
< 0.1%
477.99 1
< 0.1%
477.21 1
< 0.1%
472.85 1
< 0.1%
469 1
< 0.1%
465.08 1
< 0.1%
464.22 2
< 0.1%
460.67 1
< 0.1%

road_capacity_utilization
Real number (ℝ)

Distinct111330
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.732619
Minimum0.40000564
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:25.531086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.40000564
5-th percentile0.71010003
Q148.127346
median63.23
Q378.72
95-th percentile95.46
Maximum100
Range99.599994
Interquartile range (IQR)30.592654

Descriptive statistics

Standard deviation27.779773
Coefficient of variation (CV)0.47298714
Kurtosis-0.022274516
Mean58.732619
Median Absolute Deviation (MAD)15.34
Skewness-0.85472825
Sum17298871
Variance771.71581
MonotonicityNot monotonic
2025-04-03T15:28:25.622178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6586
 
2.2%
64.24 61
 
< 0.1%
66.29 59
 
< 0.1%
70.38 58
 
< 0.1%
74.46 57
 
< 0.1%
79.35 56
 
< 0.1%
80.3 55
 
< 0.1%
64.46 55
 
< 0.1%
79.6 54
 
< 0.1%
60.58 54
 
< 0.1%
Other values (111320) 287441
97.6%
ValueCountFrequency (%)
0.4000056382 1
< 0.1%
0.4000232341 1
< 0.1%
0.4000810961 1
< 0.1%
0.4000859224 1
< 0.1%
0.4000954801 1
< 0.1%
0.4000971946 1
< 0.1%
0.4000981106 1
< 0.1%
0.4001139728 1
< 0.1%
0.4001192132 1
< 0.1%
0.4001347152 1
< 0.1%
ValueCountFrequency (%)
100 6586
2.2%
99.99911308 1
 
< 0.1%
99.99727229 1
 
< 0.1%
99.99698556 1
 
< 0.1%
99.99429011 1
 
< 0.1%
99.99368359 1
 
< 0.1%
99.99284056 1
 
< 0.1%
99.99259095 1
 
< 0.1%
99.99148888 1
 
< 0.1%
99.99 5
 
< 0.1%

incident_reports
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6667979
Minimum0
Maximum19
Zeros25270
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:25.704849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0009588
Coefficient of variation (CV)0.7060352
Kurtosis-0.5641835
Mean5.6667979
Median Absolute Deviation (MAD)3
Skewness0.46879224
Sum1669076
Variance16.007672
MonotonicityNot monotonic
2025-04-03T15:28:25.793515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 27069
9.2%
3 26827
9.1%
1 26026
8.8%
4 26008
8.8%
0 25270
8.6%
5 24869
8.4%
6 21573
 
7.3%
7 20635
 
7.0%
8 20413
 
6.9%
9 20171
 
6.8%
Other values (10) 55675
18.9%
ValueCountFrequency (%)
0 25270
8.6%
1 26026
8.8%
2 27069
9.2%
3 26827
9.1%
4 26008
8.8%
5 24869
8.4%
6 21573
7.3%
7 20635
7.0%
8 20413
6.9%
9 20171
6.8%
ValueCountFrequency (%)
19 476
 
0.2%
18 483
 
0.2%
17 476
 
0.2%
16 470
 
0.2%
15 493
 
0.2%
14 8074
2.7%
13 8250
2.8%
12 8107
2.8%
11 14553
4.9%
10 14293
4.9%

environmental_impact
Real number (ℝ)

Distinct116422
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.138631
Minimum20.001206
Maximum551.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:25.877298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.001206
5-th percentile31.27
Q151.97
median65.850247
Q379.13
95-th percentile155.437
Maximum551.04
Range531.03879
Interquartile range (IQR)27.16

Descriptive statistics

Standard deviation60.736399
Coefficient of variation (CV)0.78736682
Kurtosis13.071695
Mean77.138631
Median Absolute Deviation (MAD)13.560247
Skewness3.6112744
Sum22720104
Variance3688.9101
MonotonicityNot monotonic
2025-04-03T15:28:25.985282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.73 60
 
< 0.1%
59.45 60
 
< 0.1%
66.18 58
 
< 0.1%
71.31 58
 
< 0.1%
76.24 58
 
< 0.1%
71.15 58
 
< 0.1%
73.92 57
 
< 0.1%
55.78 57
 
< 0.1%
79.57 57
 
< 0.1%
67.23 57
 
< 0.1%
Other values (116412) 293956
99.8%
ValueCountFrequency (%)
20.00120634 1
< 0.1%
20.00254699 1
< 0.1%
20.00269334 1
< 0.1%
20.00270382 1
< 0.1%
20.0030394 1
< 0.1%
20.00415198 1
< 0.1%
20.00628142 1
< 0.1%
20.00677726 1
< 0.1%
20.00785538 1
< 0.1%
20.00887909 1
< 0.1%
ValueCountFrequency (%)
551.04 1
< 0.1%
549.24 1
< 0.1%
502.08 1
< 0.1%
452.865 1
< 0.1%
444.86 1
< 0.1%
439.54 1
< 0.1%
436.655 1
< 0.1%
436.545 1
< 0.1%
435.425 1
< 0.1%
428.37 1
< 0.1%

public_transport_usage
Real number (ℝ)

Distinct98033
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7885491
Minimum0.10001059
Maximum98.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:26.097076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10001059
5-th percentile0.26791753
Q10.4169633
median0.55651105
Q30.69034585
95-th percentile54.402927
Maximum98.5
Range98.399989
Interquartile range (IQR)0.27338255

Descriptive statistics

Standard deviation17.303495
Coefficient of variation (CV)2.548924
Kurtosis5.4452233
Mean6.7885491
Median Absolute Deviation (MAD)0.13672249
Skewness2.6193609
Sum1999472.1
Variance299.41096
MonotonicityNot monotonic
2025-04-03T15:28:26.199023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44 4252
 
1.4%
0.69 4245
 
1.4%
0.66 4215
 
1.4%
0.57 4212
 
1.4%
0.5 4199
 
1.4%
0.68 4195
 
1.4%
0.51 4186
 
1.4%
0.53 4150
 
1.4%
0.63 4142
 
1.4%
0.61 4135
 
1.4%
Other values (98023) 252605
85.8%
ValueCountFrequency (%)
0.1000105866 1
< 0.1%
0.1000275474 1
< 0.1%
0.100042766 1
< 0.1%
0.1000634659 1
< 0.1%
0.1001249456 1
< 0.1%
0.1001266003 1
< 0.1%
0.1001367821 1
< 0.1%
0.100158952 1
< 0.1%
0.1001924626 1
< 0.1%
0.1002099402 1
< 0.1%
ValueCountFrequency (%)
98.5 1
< 0.1%
95.45 1
< 0.1%
91.8 1
< 0.1%
85.7 1
< 0.1%
80 1
< 0.1%
79.99 2
< 0.1%
79.98 1
< 0.1%
79.97974409 1
< 0.1%
79.97712437 1
< 0.1%
79.97 2
< 0.1%

traffic_signal_compliance
Real number (ℝ)

Distinct155737
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.543748
Minimum60
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:26.301559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile67.66
Q176.800438
median84.13
Q390.880829
95-th percentile97.196484
Maximum100
Range40
Interquartile range (IQR)14.080392

Descriptive statistics

Standard deviation9.096709
Coefficient of variation (CV)0.10888558
Kurtosis-0.69023871
Mean83.543748
Median Absolute Deviation (MAD)7.0306237
Skewness-0.2792356
Sum24606641
Variance82.750115
MonotonicityNot monotonic
2025-04-03T15:28:26.410078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.67 68
 
< 0.1%
75.87 67
 
< 0.1%
84.12 67
 
< 0.1%
78.88 66
 
< 0.1%
77.57 66
 
< 0.1%
82.74 65
 
< 0.1%
93.06 65
 
< 0.1%
81.34 64
 
< 0.1%
91.86 64
 
< 0.1%
81.95 64
 
< 0.1%
Other values (155727) 293880
99.8%
ValueCountFrequency (%)
60 4
 
< 0.1%
60.0039329 1
 
< 0.1%
60.00531304 1
 
< 0.1%
60.00720156 1
 
< 0.1%
60.00846571 1
 
< 0.1%
60.00981162 1
 
< 0.1%
60.01 9
< 0.1%
60.02 15
< 0.1%
60.02170809 1
 
< 0.1%
60.02333669 1
 
< 0.1%
ValueCountFrequency (%)
100 7
< 0.1%
99.99964309 1
 
< 0.1%
99.99957006 1
 
< 0.1%
99.99806868 1
 
< 0.1%
99.99802115 1
 
< 0.1%
99.99781533 1
 
< 0.1%
99.9977555 1
 
< 0.1%
99.99761074 1
 
< 0.1%
99.99677493 1
 
< 0.1%
99.99656556 1
 
< 0.1%

parking_usage
Real number (ℝ)

Distinct93626
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.980223
Minimum0.20000562
Maximum99.995049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:26.510841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.20000562
5-th percentile0.36002423
Q10.67721426
median48.275
Q364.8
95-th percentile81.568237
Maximum99.995049
Range99.795043
Interquartile range (IQR)64.122786

Descriptive statistics

Standard deviation31.604507
Coefficient of variation (CV)0.83213062
Kurtosis-1.59478
Mean37.980223
Median Absolute Deviation (MAD)27.235
Skewness-0.1226426
Sum11186543
Variance998.84486
MonotonicityNot monotonic
2025-04-03T15:28:26.602541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 776
 
0.3%
0.42 767
 
0.3%
0.44 765
 
0.3%
0.64 759
 
0.3%
0.65 758
 
0.3%
0.58 757
 
0.3%
0.63 756
 
0.3%
0.43 756
 
0.3%
0.73 753
 
0.3%
0.72 751
 
0.3%
Other values (93616) 286938
97.4%
ValueCountFrequency (%)
0.2000056206 1
< 0.1%
0.2000212135 1
< 0.1%
0.2000234995 1
< 0.1%
0.200041553 1
< 0.1%
0.2000498934 1
< 0.1%
0.2001307583 1
< 0.1%
0.2001384275 1
< 0.1%
0.200146107 1
< 0.1%
0.2001508205 1
< 0.1%
0.2001517916 1
< 0.1%
ValueCountFrequency (%)
99.99504885 1
< 0.1%
99.99025721 1
< 0.1%
99.98281545 1
< 0.1%
99.98116081 1
< 0.1%
99.97852416 1
< 0.1%
99.97775261 1
< 0.1%
99.97159879 1
< 0.1%
99.97141914 1
< 0.1%
99.95893732 1
< 0.1%
99.95189215 1
< 0.1%

pedestrian_cyclist_count
Real number (ℝ)

Distinct24323
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2055.5193
Minimum50
Maximum7999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2025-04-03T15:28:26.700503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile111
Q1374
median1358
Q33551
95-th percentile5565
Maximum7999
Range7949
Interquartile range (IQR)3177

Descriptive statistics

Standard deviation1902.5373
Coefficient of variation (CV)0.92557502
Kurtosis-0.52089011
Mean2055.5193
Median Absolute Deviation (MAD)1132
Skewness0.77708713
Sum6.0542444 × 108
Variance3619648.3
MonotonicityNot monotonic
2025-04-03T15:28:26.792576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 450
 
0.2%
97 440
 
0.1%
99 425
 
0.1%
98 425
 
0.1%
104 425
 
0.1%
101 416
 
0.1%
103 415
 
0.1%
94 410
 
0.1%
102 401
 
0.1%
96 396
 
0.1%
Other values (24313) 290333
98.6%
ValueCountFrequency (%)
50 120
< 0.1%
51 135
< 0.1%
52 128
< 0.1%
53 138
< 0.1%
54 123
< 0.1%
55 113
< 0.1%
56 118
< 0.1%
57 117
< 0.1%
58 112
< 0.1%
59 132
< 0.1%
ValueCountFrequency (%)
7999 4
< 0.1%
7998 2
< 0.1%
7997 2
< 0.1%
7996 3
< 0.1%
7995 4
< 0.1%
7994 2
< 0.1%
7993 2
< 0.1%
7992 2
< 0.1%
7990 3
< 0.1%
7989 1
 
< 0.1%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Clear
104635 
Rainy
62084 
Cloudy
53800 
Hazy
23898 
Foggy
18694 
Other values (8)
31425 

Length

Max length13
Median length5
Mean length5.3286661
Min length3

Characters and Unicode

Total characters1569484
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRainy
2nd rowHazy
3rd rowCloudy
4th rowHazy
5th rowHazy

Common Values

ValueCountFrequency (%)
Clear 104635
35.5%
Rainy 62084
21.1%
Cloudy 53800
18.3%
Hazy 23898
 
8.1%
Foggy 18694
 
6.3%
Storm 11815
 
4.0%
Thunderstorms 7629
 
2.6%
Stormy 4727
 
1.6%
Dusty 3744
 
1.3%
Overcast 1296
 
0.4%
Other values (3) 2214
 
0.8%

Length

2025-04-03T15:28:26.874295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 104635
35.5%
rainy 62084
21.1%
cloudy 53800
18.3%
hazy 23898
 
8.1%
foggy 18694
 
6.3%
storm 11815
 
4.0%
thunderstorms 7629
 
2.6%
stormy 4727
 
1.6%
dusty 3744
 
1.3%
overcast 1296
 
0.4%
Other values (3) 2214
 
0.8%

Most occurring characters

ValueCountFrequency (%)
a 192740
12.3%
y 167375
10.7%
C 158435
10.1%
l 158435
10.1%
r 137731
8.8%
e 113560
 
7.2%
o 97624
 
6.2%
n 70968
 
4.5%
u 65173
 
4.2%
i 63339
 
4.0%
Other values (17) 344104
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1569484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 192740
12.3%
y 167375
10.7%
C 158435
10.1%
l 158435
10.1%
r 137731
8.8%
e 113560
 
7.2%
o 97624
 
6.2%
n 70968
 
4.5%
u 65173
 
4.2%
i 63339
 
4.0%
Other values (17) 344104
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1569484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 192740
12.3%
y 167375
10.7%
C 158435
10.1%
l 158435
10.1%
r 137731
8.8%
e 113560
 
7.2%
o 97624
 
6.2%
n 70968
 
4.5%
u 65173
 
4.2%
i 63339
 
4.0%
Other values (17) 344104
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1569484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 192740
12.3%
y 167375
10.7%
C 158435
10.1%
l 158435
10.1%
r 137731
8.8%
e 113560
 
7.2%
o 97624
 
6.2%
n 70968
 
4.5%
u 65173
 
4.2%
i 63339
 
4.0%
Other values (17) 344104
21.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size287.8 KiB
False
171047 
True
123489 
ValueCountFrequency (%)
False 171047
58.1%
True 123489
41.9%
2025-04-03T15:28:27.057345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-04-03T15:28:20.651690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.415167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.437189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.392724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.356421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.302938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.411089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.385817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.345504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.403281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.724807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.748299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.513070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.525352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.491755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.435646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.390753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.508654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.475988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.425311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.519856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.812390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.834046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.597254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.611444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.579877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.519639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.474399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.588388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.570646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.508425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.628962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.898751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.932575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.690942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.688139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.663481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.601412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.566515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.674044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.653047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.608771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.727903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.976442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.023947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.783564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.782128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.754748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.685980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.653553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.761708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.738514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.707464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.824626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.060471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.127802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.886219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.869212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.845645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.769897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.741042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.840097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.818558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.804554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.930191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.138511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.232644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:10.986583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.958324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.935198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.862016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.953039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.929254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.910573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.904575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.032511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.226744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.324190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.087083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.041339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.017028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.939170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.045155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.019833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.989220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.000972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.271971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.298398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.424056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.174851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.126644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.098017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.020594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.133905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.118136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.074288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.091497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.370652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.384648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.531122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.259353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.222287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.186227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.120532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.223424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.205253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.167039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.199758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.479834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.471814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:21.650035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:11.346776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:12.303137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:13.271068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:14.210986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:15.309708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:16.298520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:17.250803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:18.298551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:19.606410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-03T15:28:20.553638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-03T15:28:27.098929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
average_speedcongestion_levelenvironmental_impactincident_reportsparking_usagepedestrian_cyclist_countpublic_transport_usageroad_capacity_utilizationroadwork_constructiontraffic_signal_compliancetraffic_volumetravel_time_indexweather_conditions
average_speed1.000-0.2520.001-0.0000.3170.245-0.0470.1210.096-0.115-0.019-0.2130.089
congestion_level-0.2521.0000.2190.0160.0850.1260.1390.1700.0550.0710.2900.1450.109
environmental_impact0.0010.2191.000-0.0530.2710.0470.2760.1940.2550.0320.3460.0400.135
incident_reports-0.0000.016-0.0531.000-0.0400.181-0.147-0.1030.0530.029-0.1090.0830.066
parking_usage0.3170.0850.271-0.0401.0000.3210.2160.3390.262-0.1610.137-0.1960.171
pedestrian_cyclist_count0.2450.1260.0470.1810.3211.000-0.1970.0580.167-0.057-0.0470.0870.150
public_transport_usage-0.0470.1390.276-0.1470.216-0.1971.0000.1390.1830.0240.231-0.0540.142
road_capacity_utilization0.1210.1700.194-0.1030.3390.0580.1391.0000.270-0.0310.418-0.1240.145
roadwork_construction0.0960.0550.2550.0530.2620.1670.1830.2701.0000.0960.2760.0740.123
traffic_signal_compliance-0.1150.0710.0320.029-0.161-0.0570.024-0.0310.0961.0000.0530.0860.083
traffic_volume-0.0190.2900.346-0.1090.137-0.0470.2310.4180.2760.0531.0000.0640.109
travel_time_index-0.2130.1450.0400.083-0.1960.087-0.054-0.1240.0740.0860.0641.0000.109
weather_conditions0.0890.1090.1350.0660.1710.1500.1420.1450.1230.0830.1090.1091.000

Missing values

2025-04-03T15:28:21.868611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-03T15:28:22.254372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datearea_nameroad_nametraffic_volumeaverage_speedtravel_time_indexcongestion_levelroad_capacity_utilizationincident_reportsenvironmental_impactpublic_transport_usagetraffic_signal_complianceparking_usagepedestrian_cyclist_countweather_conditionsroadwork_construction
02022-01-01Gomti NagarShaheed Path1882325.3995473.2252.72786388.72772.870.5783.2355.363867.0RainyNo
12022-01-01Indira NagarFaizabad Road708338.8665772.9548.71510883.10684.590.5287.8671.314730.0HazyNo
22022-01-01AlambaghKanpur Road (NH-27)3793357.6813442.2962.92928389.11765.470.4990.3356.222783.0CloudyNo
32022-01-01RajajipuramHardoi Road3870756.8979322.8880.80175553.37064.750.7382.6350.692921.0HazyYes
42022-01-01ArjunganjSultanpur Road969954.2195562.5341.40115880.78356.760.6294.6768.883282.0HazyYes
52022-01-01MahanagarSitapur Road3909459.8112403.3525.13763872.01450.320.6067.7168.772427.0FoggyNo
62022-01-01Gomti Nagar ExtensionAmar Shaheed Path2453444.6648241.7977.18565065.68358.430.4790.9875.664782.0CloudyYes
72022-01-01JankipuramRing Road648051.3399873.2453.86813071.91952.170.4385.7760.702905.0HazyYes
82022-01-01HazratganjVidhan Sabha Marg311222.4230031.9249.65442853.05179.860.7495.0863.674733.0FoggyYes
92022-01-01Gomti NagarLohia Path2240147.7116351.7566.81859565.60581.820.7169.1159.741006.0CloudyYes
datearea_nameroad_nametraffic_volumeaverage_speedtravel_time_indexcongestion_levelroad_capacity_utilizationincident_reportsenvironmental_impactpublic_transport_usagetraffic_signal_complianceparking_usagepedestrian_cyclist_countweather_conditionsroadwork_construction
2945262024-08-08HebbalBallari Road1056340.2791421.38377530.46340849.487880071.12654.59636196.48125683.048212187.0ClearNo
2945272024-08-09Indiranagar100 Feet Road3333924.9251431.500000100.000000100.0000001116.67813.55988681.75532053.851126107.0ClearNo
2945282024-08-09IndiranagarCMH Road3726538.4717681.500000100.000000100.0000004124.53070.58637064.66804095.563755112.0ClearNo
2945292024-08-09KoramangalaSony World Junction2767320.0000001.50000093.969031100.0000000105.34657.72652088.79591783.96588996.0OvercastYes
2945302024-08-09KoramangalaSarjapur Road5899243.1384511.500000100.000000100.0000002167.98468.81080469.85925764.179922101.0ClearNo
2945312024-08-09Electronic CityHosur Road1138723.4402761.26238435.87148357.354487172.77421.52328983.53035297.898279211.0FogNo
2945322024-08-09M.G. RoadTrinity Circle3647745.1684291.500000100.000000100.0000003122.95429.82231260.73848860.35596795.0ClearNo
2945332024-08-09M.G. RoadAnil Kumble Circle4282222.0286091.500000100.000000100.0000001135.64443.18590585.32162761.333731110.0ClearNo
2945342024-08-09JayanagarSouth End Circle2054052.2547981.02052072.63915297.845527291.08044.41604389.58694779.19719894.0ClearNo
2945352024-08-09YeshwanthpurYeshwanthpur Circle1470531.1289671.04872043.40982177.734621179.41026.61672580.77875360.602672201.0RainNo